Progressive Fourier Neural Representation for Sequential Video Compilation

Neural Implicit Representation (NIR) has recently gained significant attention due to its remarkable ability to encode complex and high-dimensional data into representation space and easily reconstruct it through a trainable mapping function. However, NIR methods assume a one-to-one mapping between the target data and representation models regardless of data relevancy or similarity. This results in poor generalization over multiple complex data and limits their efficiency and scalability. Motivated by continual learning, this work investigates how to accumulate and transfer neural implicit representations for multiple complex video data over sequential encoding sessions. To overcome the limitation of NIR, we propose a novel method, Progressive Fourier Neural Representation (PFNR), that aims to find an adaptive and compact sub-module in Fourier space to encode videos in each training session. This sparsified neural encoding allows the neural network to hold free weights, enabling an improved adaptation for future videos. In addition, when learning a representation for a new video, PFNR transfers the representation of previous videos with frozen weights. This design allows the model to continuously accumulate high-quality neural representations for multiple videos while ensuring lossless decoding that perfectly preserves the learned representations for previous videos. We validate our PFNR method on the UVG8/17 and DAVIS50 video sequence benchmarks and achieve impressive performance gains over strong continual learning baselines. The PFNR code is available at https://github.com/ihaeyong/PFNR.git

Improving Neural Radiance Field using Near-Surface Sampling with Point Cloud Generation

Neural radiance field (NeRF) is an emerging view synthesis method that samples points in a three-dimensional (3D) space and estimates their existence and color probabilities. The disadvantage of NeRF is that it requires a long training time since it samples many 3D points. In addition, if one samples points from occluded regions or in the space where an object is unlikely to exist, the rendering quality of NeRF can be degraded. These issues can be solved by estimating the geometry of 3D scene. This paper proposes a near-surface sampling framework to improve the rendering quality of NeRF. To this end, the proposed method estimates the surface of a 3D object using depth images of the training set and sampling is performed around there only. To obtain depth information on a novel view, the paper proposes a 3D point cloud generation method and a simple refining method for projected depth from a point cloud. Experimental results show that the proposed near-surface sampling NeRF framework can significantly improve the rendering quality, compared to the original NeRF and a state-of-the-art depth-based NeRF method. In addition, one can significantly accelerate the training time of a NeRF model with the proposed near-surface sampling framework.Comment: 13 figures, 2 table

Forget-free Continual Learning with Soft-Winning SubNetworks

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we investigate two proposed architecture-based continual learning methods which sequentially learn and select adaptive binary- (WSN) and non-binary Soft-Subnetworks (SoftNet) for each task. WSN and SoftNet jointly learn the regularized model weights and task-adaptive non-binary masks of subnetworks associated with each task whilst attempting to select a small set of weights to be activated (winning ticket) by reusing weights of the prior subnetworks. Our proposed WSN and SoftNet are inherently immune to catastrophic forgetting as each selected subnetwork model does not infringe upon other subnetworks in Task Incremental Learning (TIL). In TIL, binary masks spawned per winning ticket are encoded into one N-bit binary digit mask, then compressed using Huffman coding for a sub-linear increase in network capacity to the number of tasks. Surprisingly, in the inference step, SoftNet generated by injecting small noises to the backgrounds of acquired WSN (holding the foregrounds of WSN) provides excellent forward transfer power for future tasks in TIL. SoftNet shows its effectiveness over WSN in regularizing parameters to tackle the overfitting, to a few examples in Few-shot Class Incremental Learning (FSCIL).Comment: arXiv admin note: text overlap with arXiv:2209.0752

Adaptive laboratory evolution of a genome-reduced Escherichia coli.

Synthetic biology aims to design and construct bacterial genomes harboring the minimum number of genes required for self-replicable life. However, the genome-reduced bacteria often show impaired growth under laboratory conditions that cannot be understood based on the removed genes. The unexpected phenotypes highlight our limited understanding of bacterial genomes. Here, we deploy adaptive laboratory evolution (ALE) to re-optimize growth performance of a genome-reduced strain. The basis for suboptimal growth is the imbalanced metabolism that is rewired during ALE. The metabolic rewiring is globally orchestrated by mutations in rpoD altering promoter binding of RNA polymerase. Lastly, the evolved strain has no translational buffering capacity, enabling effective translation of abundant mRNAs. Multi-omic analysis of the evolved strain reveals transcriptome- and translatome-wide remodeling that orchestrate metabolism and growth. These results reveal that failure of prediction may not be associated with understanding individual genes, but rather from insufficient understanding of the strain's systems biology

On the Soft-Subnetwork for Few-shot Class Incremental Learning

Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive performance of the dense network, we propose a few-shot class incremental learning (FSCIL) method referred to as \emph{Soft-SubNetworks (SoftNet)}. Our objective is to learn a sequence of sessions incrementally, where each session only includes a few training instances per class while preserving the knowledge of the previously learned ones. SoftNet jointly learns the model weights and adaptive non-binary soft masks at a base training session in which each mask consists of the major and minor subnetwork; the former aims to minimize catastrophic forgetting during training, and the latter aims to avoid overfitting to a few samples in each new training session. We provide comprehensive empirical validations demonstrating that our SoftNet effectively tackles the few-shot incremental learning problem by surpassing the performance of state-of-the-art baselines over benchmark datasets

Molecular cloning and expression of a novel human cDNA related to the diazepam binding inhibitor

AbstractIn order to isolate the unidentified autoantigens in autoimmune diabetes, a human pancreatic islet cDNA library was constructed and screened with the sera from the diabetic patients. From the library screening, one clone (DRS-1) that strongly reacted with the sera was isolated. Subsequent sequence analysis revealed that the clone was a novel cDNA related to the diazepam binding inhibitor. DRS-1 was expressed in most tissues including liver, lung, tonsil, and thymus, in addition to pancreatic islets. DRS-1 was in vitro translated and the recombinant DRS-1 protein was expressed in Escherichia coli and purified. The size of the in vitro translated or bacterially expressed DRS-1 protein was in agreement with the conceptually translated polypeptide of DRS-1 cDNA. Further studies are required to test whether or not DRS-1 is a new autoantigen in autoimmune diabetes

A Spherical Hybrid Triboelectric Nanogenerator for Enhanced Water Wave Energy Harvesting

Water waves are a continuously generated renewable source of energy. However, their random motion and low frequency pose significant challenges for harvesting their energy. Herein, we propose a spherical hybrid triboelectric nanogenerator (SH-TENG) that efficiently harvests the energy of low frequency, random water waves. The SH-TENG converts the kinetic energy of the water wave into solid-solid and solid-liquid triboelectric energy simultaneously using a single electrode. The electrical output of the SH-TENG for six degrees of freedom of motion in water was investigated. Further, in order to demonstrate hybrid energy harvesting from multiple energy sources using a single electrode on the SH-TENG, the charging performance of a capacitor was evaluated. The experimental results indicate that SH-TENGs have great potential for use in self-powered environmental monitoring systems that monitor factors such as water temperature, water wave height, and pollution levels in oceans.11Ysciescopu

Application and evaluation of the MLVA typing assay for the Brucella abortus strains isolated in Korea

<p>Abstract</p> <p>Background</p> <p>A Brucella eradication program has been executed in Korea. To effectively prevent and control brucellosis, a molecular method for genetic identification and epidemiological trace-back must be established. As part of that, the MLVA typing assay was evaluated and applied to <it>B. abortus </it>isolates for analyzing the characteristics of the regional distribution and relationships of foreign isolates.</p> <p>Results</p> <p>A total of 177 isolates originating from 105 cattle farms for the period 1996 to 2008 were selected as representatives for the nine provinces of South Korea. A dendrogram of strain relatedness was constructed in accordance with the number of tandem repeat units for 17 loci so that it was possible to trace back in the restricted areas. Even in a farm contaminated by one source, however, the <it>Brucella </it>isolates showed an increase or decrease in one TRs copy number at some loci with high DI values. Moreover, those 17 loci was confirmed in stability via <it>in-vitro </it>and <it>in-vivo </it>passage, and found to be sufficiently stable markers that can readily identify the inoculated strain even if minor changes were detected. In the parsimony analysis with foreign <it>Brucella </it>isolates, domestic isolates were clustered distinctively, and located near the Central and Southern American isolates.</p> <p>Conclusion</p> <p>The MLVA assay has enough discrimination power in the <it>Brucella </it>species level and can be utilized as a tool for the epidemiological trace-back of the <it>B. abortus </it>isolates. But it is important to consider that <it>Brucella </it>isolates may be capable of undergoing minor changes at some loci in the course of infection or in accordance with the changes of the host.</p

Annealing Effect of ZnO Seed Layer on Enhancing Photocatalytic Activity of ZnO/TiO 2

Zinc oxide (ZnO)/titanium dioxide (TiO2) nanorods have been synthesized via a hydrothermal method for ZnO nanorods and an electron-beam deposition for TiO2 nanorods. This work examined the effect of annealing ZnO seed layer on the photocatalytic activity of the ZnO/TiO2 nanorods which was determined from photodecomposition of methylene blue under UV irradiation. The photocatalytic activity of the ZnO/TiO2 nanorods was improved with increasing annealing temperature of the seed layer from 300°C to 500°C. Annealing the seed layer at 500°C showed the best photocatalytic activity resulting from high UV absorption ability, a large surface area with flower structure and copious oxygen defects which promote separation of electron-hole pairs reducing electron recombination. The prepared nanorods were characterized by field emission-scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), photoluminescence (PL), and UV-visible spectroscopy